Producing nine data visualisations seemed like a daunting task at the beginning. After a while, however, I felt more confident and I hope my development is reflected in the visualisations. Rather than tracking every-day tasks, I tried to focus on data related to my studies. Initially, I thought that this would give me a better insight into how data are used for learning and teaching, but towards the end of the nine weeks, I came to realise that it was the act of collecting data and drawing that was important and not the subject.
Visualising data and its challenges
For me the main challenge of this task wasn’t usually how to present the data but what to measure. I often felt restricted by what I could record as manually collecting data can be time consuming. Other times, I was mindful that if I was tracking a lot of data, my visualisations may become too complex. What this taught me, however, was that data scientists and other professionals involved in using educational data are likely to be facing similar restrictions. While they have the ability to collect vast amount of data, they will have to decide what a student dashboard displays, for example.
Block 1 – ‘Learning’ with data
The first block challenged us to critically think about how student learning is influenced by data-driven technologies and processes, how data can enhance student learning and what issues may arise from these practices.
Learning analytics and data science can give students, teachers and institutions valuable insights into their behaviour and practices. Knox et al. (2020, p.34) highlight that ‘the promotion of learning analytics is often premised upon its ability to reveal insights about learning unobtainable without the collection and analysis of learner-data’. Others argue that ‘simply observing learning events is not revealing of successful and unsuccessful learning patterns’ (Selwyn & Gašević 2020, p. 529). While drawing up my data visualisations, I was often mindful of how revealing they really were. After selecting what data to analyse and how to present them, I was aware of the number of choices I made during the process and the effect this had on objectivity.
During this course, I was also often aware of how much information about myself I am giving away and who might be able to access it. This raised the question of how can we find a balance between learning analytics enhancing student agency and the increased surveillance and control of students (Tsai et al. 2020). As surfaced in Tsai et al. (2020, p. 556), there are further ethical concerns such as ‘the dangerous tendency of predictive modelling to reproduce existing biases based on race, gender and class.
Block 2 – ‘Teaching’ with data
This block looked at the role of data in teaching and their impact on teachers and teaching practices. Due to the rise in big data and learning analytics, the use of data goes far beyond of providing teachers with information about their students’ learning progress. As surfaced in Williamson et al. (2020, p.354), ‘measures of student performance, sentiment, engagement, and satisfaction are also treated as proxy measures of the performance of staff, courses, schools, and institutions as a whole, leading to new claims that HE quality can be adduced from the analysis of large-scale student data.’ This development may have serious implications on teachers’ autonomy and creativity, particularly since ‘not all forms of learning can be quantified and analysed. And this means, potentially, that not all forms of teaching and learning will ‘count’ in terms of how teachers and students are measured and assessed’ (Williamson et al. 2020, p. 358).
Institutions should be careful to align learning analytics technologies with pedagogical theories and not only consider developers’ priorities which often have profits in mind (Raffaghelli & Stewart, 2020).
Block 3 – ‘Governing’ with data
By the time I reached block 3, I struggled to think of what data to visualise. Once I found a topic, however, the task of collecting information and producing my visualisation felt more natural.
What particularly stood out for me in my visualisation of our Tweetorial was that governing in education is no longer a political matter only. Organisations such as Microsoft, Google and OECD were mentioned during our exercise, indicating that a variety of actors are now involved in policy-making. Williamson illustrates that ‘alongside the rising use of data, education has experienced a ‘governance turn’ which sees authority over educational redistributed from central governments and their agencies to a much wider array of private sector and civil organizations, including businesses, consultants, entrepreneurs, think tanks, policy innovation labs, charities and independent experts, many of them tangled together in networks of relationships’ (Williamson 2017, p.67). We have to ask ourselves what motives are behind this investment in education and what the consequences are for teachers and learners.
Another issues that became apparent during this block were the reliance on data to assess performance (Ozga, 2016) and the rise of standardisation (Anagnostopoulos et al., 2013). While introducing standardised tests and policies can highlight opportunities in educational policies, there are also risks. ‘As large-scale information systems produce increasingly precise measurements of student, teacher, and school performance, they risk substituting precision for validity and distracting from important issues, such as educational equity, diversity, and social justice, that are not easily reduced to or redressed by standardized metrics’ (Anagnostopoulos et al. 2013, p. 16).
Each block has highlighted different aspects and issues of data in education but one of the recurring themes was the question of objectivity. Data, and the way they are presented, are often perceived to be neutral and used to back up findings, and produce recommendations. Deciding which data to collect and how to visualise them, has strongly emphasised that data carries a large amount of subjectivity. They contain our personal values, beliefs and knowledge and visualisations are therefore selective and biased.
Examining the bigger picture of educational data in the governing block, I became aware of the increasing power data have in education. Large organisations such as the OECD now have the ability to influence policies at a global scale, potentially putting specialised knowledge at risk.
I have enjoyed examining the role of data in education from a critical perspective and now feel more equipped at recognising potential issues, however, as someone working in digital education, I am still unsure of how to use data well. Nonetheless, not blindly trusting data and questioning practices is something that I am now able to do and which should be a skill for everyone working in education.
Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (2013). Introduction: Mapping the Information Infrastructure of Accountability. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds.) The Infrastructure of Accountability: Data use and the transformation of American education.
Knox, J., Williamson, B. & Bayne, S. (2020). ‘Machine behaviourism: Future visions of “learnification” and “datafication” across humans and digital technologies‘, Learning, Media and Technology, 45 (1), pp. 1-15.
Ozga J. (2016). Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal. 15(1), 69-81. doi:10.1177/1474904115616629
Raffaghelli, J.E. & Stewart, B. (2020). Centering complexity in ‘educators’ data literacy’ to support future practices in faculty development: a systematic review of the literature, Teaching in Higher Education, 25:4, 435-455, DOI: 10.1080/13562517.2019.1696301.
Selwyn, N. & Gašević, D. (2020). The datafication of higher education: discussing the promises and problems, Teaching in Higher Education, 25:4, 527-540, DOI: 10.1080/13562517.2019.1689388
Tsai, Y-S. Perrotta, C. & Gašević, D., (2020). Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics, Assessment & Evaluation in Higher Education, 45 (4), pp. 554-567, DOI: 10.1080/02602938.2019.1676396
Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. SAGE Publications Ltd, https://www.doi.org/10.4135/9781529714920
Williamson, B. Bayne, S. Shay, S. (2020). The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education. 25 (4), 351-365.